A Walk Through the Random Forest

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-sp19-random-forests").exists():
    DATA_DIR /= "ucfai-core-sp19-random-forests"
    # You'll need to download the data from Kaggle and place it in the `data/`
    #   directory beside this notebook.
    # The data should be here: https://kaggle.com/c/ucfai-core-sp19-random-forests/data
    DATA_DIR = Path("data")

Other Meetings in this Series

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In the fields of Data Science and Artificial Intelligence, your models and analyses will only be as good as the data behind them. Unfortunately, you will find that the majority of datasets you encounter will be filled with missing, malformed, or erroneous data. Thankfully, Python provides a number of handy libraries to help you clean and manipulate your data into a usable state. In today's lecture, we will leverage these Python libraries to turn a messy dataset into a gold mine of value!

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Support Vector Machines were among the most highly used ML algorithms before Neural Nets came back into the foreground. Unlike Neural Nets, SVMs can explain themselves quite well and allow us to use these ML mdels in fields like medicine, finance, and the like – where regulations require that we can inquire about our models.

Contributing Authors

John Muchovej
John Muchovej

Founder of AI@UCF. Researcher in cognitive science and machine learning. Focusing on intuitive physics and intuitive psychology.